An assessment of evidence to inform best practice for the communication of acute venous thromboembolism diagnosis: a scoping review
Bibliographic record
Abstract
Background: Physician communication with patients is a key aspect of excellent care. Scant evidence exists to inform best practice for physician communication in patients diagnosed with pulmonary embolism and deep vein thrombosis, collectively referred to as venous thromboembolism (VTE). Objectives: The aim of this study was to summarize the existing literature on best practices for communication between healthcare providers and patients newly diagnosed with VTE. Methods: We performed a scoping review to report existing literature on best practices for physician-patient communication and the diagnosis and management of acute VTE. Manuscripts on communication between healthcare professionals and patients presenting with acute VTE and acute vascular disease presentations that included atrial fibrillation and acute coronary syndrome were identified. Two authors independently reviewed studies for eligibility and a consensus determined article inclusion. The manuscripts were further categorized into 2 categories: best practices in communication and unmet needs in communication. Data aggregation was achieved by a modified thematic synthesis. Results: Among 345 initial publications, 22 manuscripts met inclusion criteria, with 11 addressing VTE, 5 pulmonary embolism, 4 deep vein thrombosis, 1 atrial fibrillation, and 1 acute coronary syndrome. Eleven manuscripts addressed communication of VTE diagnosis, while 12 focused on communication of VTE treatment. Eleven manuscripts identified unmet communication needs, and 14 addressed best practices. Our review showed that good communication enhanced satisfaction, while suboptimal communication was associated with emotional, cognitive, behavioral, social, and health systems adverse effects. Conclusion: Scant literature guides best practices for communicating VTE diagnosis and treatment. Further research is necessary to establish practices for improving communication with VTE patients.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.130 | 0.412 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.012 | 0.014 |
| Bibliometrics | 0.057 | 0.035 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.012 | 0.014 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".